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Using a hybrid agent-based and equation based model to test school closure policies during a measles outbreak
Elizabeth Hunter1, John D Kelleher2
1Technological University Dublin, Grangegorman, Dublin, Ireland. elizabeth.hunter@tudublin.ie.
BMC Public Health
|March 13, 2021
Summary
Closing schools in towns with high network centrality significantly reduces infectious disease outbreaks. This strategy is more effective than closing schools based on proximity to the initial outbreak, minimizing spread and impact.
Area of Science:
- Epidemiology and Public Health
- Computational Modeling
- Network Science
Background:
- Effective infectious disease outbreak preparedness requires understanding intervention impacts.
- Computational modeling is crucial for estimating intervention effectiveness.
- Comparing intervention scenarios to control runs identifies optimal strategies.
Purpose of the Study:
- To evaluate the impact of school closure policies on infectious disease spread.
- To compare school closure strategies based on geographic proximity versus network centrality.
Main Methods:
- Utilized a hybrid agent-based and equation-based model for disease simulation.
- Simulated measles outbreak scenarios with targeted school closures.
- Analyzed outbreak metrics: number of outbreaks, infected agents, and geographic spread.
Main Results:
- Closing schools in the initial outbreak town and the highest in-degree centrality town maximally reduced outbreak occurrences and geographic spread.
- School closures based on proximity to the initial outbreak showed some reduction but were less effective than centrality-based closures.
- High in-degree centrality town school closures significantly outperformed proximity-based closures.
Conclusions:
- Prioritizing school closures in high in-degree centrality towns is vital for mitigating large-scale outbreaks.
- Network centrality is a key factor in designing effective infectious disease intervention strategies.
- Computational modeling provides valuable insights for public health preparedness.
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